Model based grouping of species across environmental gradients

Ecological Modelling

Volumn 222, Issue 4, 2011, Pages 955-963

  Dunstan, Piers K ^a   Foster, Scott D ^a   Darnell, Ross ^a  

^a CSIRO   (Australia)

Author keywords

Biodiversity; Finite mixture model; Grouping; Prediction; Species archetype

Indexed keywords

ARTIFICIAL DATA; DISTRIBUTION PATTERNS; ENVIRONMENTAL GRADIENT; FINITE MIXTURE MODELS; GREAT BARRIER REEF; GROUPING; MINIMAL INFORMATION; MODEL-BASED; MULTI-SPECIES; MULTIPLE SPECIES; NUMBER OF SPECIES; POTENTIAL APPLICATIONS; PREDICTION; SPECIES ARCHETYPE; STATISTICAL ANALYSIS; STATISTICAL MODELS; STUDY AREAS;

BIODIVERSITY; MIXTURES; OXYGEN;

FORECASTING;

BARRIER REEF; DATA INTERPRETATION; ECOLOGICAL MODELING; ENVIRONMENTAL GRADIENT; GEOGRAPHICAL DISTRIBUTION; GROUP BEHAVIOR; NUMERICAL MODEL; SALINITY; SPECIES DIVERSITY; STATISTICAL ANALYSIS; UNCERTAINTY ANALYSIS;

CORAL SEA; GREAT BARRIER REEF LAGOON; PACIFIC OCEAN;

EID: 78651421794     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2010.11.030     Document Type: Article

References (42)

1. Aitchison J. The Statistical Analysis of Compositional Data 1986, Chapman & Hall, New York.
2. Aitkin M., Aitkin I. A hybrid EM/Gauss-Newton algorithm for maximum likelihood in mixture distributions. Statistics and Computing 1996, 6:127-130.
3. Austin M., Belbin L. A new approach to the species classification problem in floristic analysis. Australian Journal of Ecology 1982, 7:75-89.
4. Bellwood D., Wainwright P. Locomotion in labrid fishes: implications for habitat use and cross-shelf biogeography on the great barrier reef. Coral Reefs 2001, 20(2):139-150.
5. Bojorques-Tapia L., Azuara I., Ezcurra E., Flores-Villela O. Identifying conservation priorities in Mexico through geographic informational systems and modelling. Ecological Applications 1995, 51:215-231.
6. Chase J.M., Leibold M.A. Ecological Niches. Linking Classical and Contemporary Approaches 2003, The University of Chicago Press, Chicago/London.
7. Clark J. Individuals and the variation needed for high species diversity in forest trees. Science 2010, 327:1129-1132.
8. Clark J.M.D., Chakraborty S., Agarwal P., Ibanez I., LaDeau S., Wolosin M. Resolving the biodiversity paradox. Ecology Letters 2007, 10:647-662.
9. De'ath G. Multivariate regression trees: a new technique for modelling species - environment relationships. Ecology 2002, 83(4):1105-1117.
10. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society - Series B 1977, 39:1-38.
11. Dunn J.R., Ridgway K.R. Mapping ocean properties in regions of complex topography. Deep Sea Research I: Oceanographic Research 2002, 49(3):591-604.
12. Elith J.H., Graham C.P., Anderson R., Dudik M., Ferrier S., Guisan A.J., Hijmans R., Huettmann F.R., Leathwick J., Lehmann A., et al. Novel methods improve prediction of species' distributions from occurrence data. Ecography 2006, 29(2):129-151.
13. Faith D.P., Minchin P.R., Belbin L. Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 1987, 69:57-68.
14. Ferrier S., Drielsma M., Manion G., Watson G. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling. Biodiversity And Conservation 2002, 11:2309-2338.
15. Ferrier S., Guisan A. Spatial modelling of biodiversity at the community level. Journal of Applied Ecology 2006, 43(3):393-404.
16. Ferrier S., Manion G., Elith J., Richardson K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity and Distributions 2007, 13:252-264.
17. Franklin J. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography 1995, 19:474-499.
18. Friedman J. Multivariate adaptive regression splines. Annals of Statistics 1991, 19:1-67.
19. Friedman J., Hastie T., Tibshirani R. Additive logistic regression: a statistical view of boosting. Annals of Statistics 2000, 28(2):337-374.
20. Guisan A., Lehmann A., Ferrier S., Austin M., Overton J., Aspinall R., Hastie T. Making better biogeographical predictions of species' distributions. Journal of Applied Ecology 2006, 43(3):386-392.
21. Guisan A., Thuiller W. Predicting species distribution: offering more than simple habitat models. Ecology Letters 2005, 8(9):993-1009.
22. Hastie T.J., Tibshirani R.J. Generalized Additive Models 1990, Chapman & Hall/CRC.
23. Hubbell S.P. The Unified Neutral Theory of Biodiversity and Biogeography 2001, Princeton University Press, Princeton.
24. Hutchinson G. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology 1957, 22(2):415-427.
25. Johnson N.L., Kotz S., Kemp A. Univariate Discrete Distributions 1993, John Wiley & Sons, New York.
26. Khalili A., Chen J. Variable selection in finite mixture of regression models. Journal of the American Statistical Association 2007, 102:1025-1038.
27. Leathwick J.R., Rowe D., Richardson J., Elith J., Hastie T. Using multivariate adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish. Freshwater Biology 2005, 50:2034-2052.
28. Legendre P., Legendre L. Numerical Ecology 1998, Developments in Environmental Modelling. Elsevier Science. 2nd ed.
29. Leisch F. Flexmix: A general framework for finite mixture models and latent class regression in r. Journal of Statistical software 2004, 11(8):1-18.
30. McCulloch C.E., Nelder J.A. Generalized Linear Models 1989, Chapman & Hall.
31. McKenzie N., Belbin L., Margules C., Keighery G. Selecting representative reserve systems in remote areas: a case study in the Nullarbor region, Australia. Biological Conservation 1989, 50:239-261.
32. McLachlan G., Krishnan T. The EM Algorithm and Extensions 2008, Wiley. 2nd ed.
33. McLachlan G., Peel D. Finite Mixture Models 2000, Wiley.
34. Nash S.G., Sofer A. Linear and Nonlinear Programming 1996, McGraw-Hill Series in Industrial Engineering and Management Science. McGraw-Hill Inc., New York. 1st ed.
35. Ovaskainen O., Hottola J., Siitonen J. Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions. Ecology 2010, 91(9):2514-2521.
36. Pitcher C., Doherty P., Arnold P., Hooper J., Gribble N., Bartlett C., Browne M., Campbell N., Cannard T., Cappo M., Carini G., Chalmers S., Cheers S., Chetwynd D., Colefax A., Coles R., Cook S., Davie P., De'ath G., Devereux D., Done B., Donovan T., Ehrke B., Ellis N., Ericson G., Fellegara I., Forcey K., Furey M., Gledhill D., Good N., Gordon S., Haywood M., Jacobsen I., Johnson J., Jones M., Kinninmoth S., Kistle S., Last P., Leite A., Marks S., McLeod I., Oczkowicz S., Rose C., Seabright D., Sheils J., Sherlock M., Skelton P., Smith D., Smith G., Speare P., Stowar M., Strickland C., Sutcliffe P., Van der Geest C., Venables W., Walsh C., Wassenberg T., Welna A., Yearsley G. Seabed Biodiversity on the Continental Shelf of the Great Barrier Reef World Heritage Area 2007, Tech. rep., CSIRO Marine and Atmospheric Research.
37. Raftery A., Dean N. Variable selection for model-based clustering. Journal of the American Statistical Association 2006, 101:168-189.
38. Ridgway K.R., Dunn J.R., Wilkin J.L. Ocean interpolation by four-dimensional weighted least squares - application to the waters around Australia. Journal of Atmospheric and Oceanic Technology 2002, 19(9):1357-1375.
39. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 1978, 6:461-464.
40. Steneck R., Dethier M. A functional-group approach to the structure of algal-dominated communities. Oikos 1994, 69(3):476-498.
41. Wedel M., DeSarbo W. A mixture likelihood approach for generalized linear models. Journal of Classification 1995, 12:2155.
42. Whittaker R.H. Gradient analysis of vegetation. Biological Reviews 1967, 49:207-264.